7.1. From Frequent Pattern Mining to Phrase Mining

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From the course by University of Illinois at Urbana-Champaign

Pattern Discovery in Data Mining

170 ratings

University of Illinois at Urbana-Champaign

Pattern Discovery in Data Mining

170 ratings

Course 4 of 6 in the Specialization Data Mining

Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for data-driven phrase mining and some interesting applications of pattern discovery. This course provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.

From the lesson

Week 4

Module 4 consists of two lessons: Lessons 7 and 8. In Lesson 7, we study mining quality phrases from text data as the second kind of pattern mining application. We will mainly introduce two newer methods for phrase mining: ToPMine and SegPhrase, and show frequent pattern mining may be an important role for mining quality phrases in massive text data. In Lesson 8, we will learn several advanced topics on pattern discovery, including mining frequent patterns in data streams, pattern discovery for software bug mining, pattern discovery for image analysis, and pattern discovery and society: privacy-preserving pattern mining. Finally, we look forward to the future of pattern mining research and application exploration.

7.1. From Frequent Pattern Mining to Phrase Mining4:08

7.2. Previous Phrase Mining Methods10:50

7.3. ToPMine: Phrase Mining without Training Data12:11

7.4. SegPhrase: Phrase Mining with Tiny Training Sets14:27

Meet the Instructors

Jiawei Han
Abel Bliss Professor
Department of Computer Science

Now we are going to discuss another interesting pattern mining applications

called Mining Quality Phrases from Text Data.

We encounter massive unstructured text data.

To get knowledge out of text data, a very important thing is mining quality phrases.

We will discuss how we do it from mining frequent patterns to mining phrases.

We'll reintroduce some previous phrase pattern mining methods, then

we are going to discuss two interesting new algorithms developed by those.

Many of my students, they are ToPMine and SegPhrase.

ToPMine is mining phrases without training data, and

SegPhrases by introducing tiny training data sets

you can further enhance the phrase mining quality.

Let's first study why we want to get from frequent pattern mining to phrase mining.

The first question you may ask is why we want to do phrase mining.

Actually, if we think every word is a unigram, and then phrases

actually are a set of consecutive words made from some meaningful semantic unit.

We probably can see, for single words, or we call unigrams, they're often ambiguous.

For example, when you say united, people often do not know

you mean United States or United Airlines or United Parcel Service?

However, once you present them as a phrase like United States,

or United Airlines, these kind of phrases are actually

natural meaningful and unambiguous semantic units.

That means if we want automatically transform text data to some meaningful

sentences or to some meaningful analysis structures,

the first important thing is mining semantically meaningful phrases?

This would transform text data analysis from

word granularity to phrase granularity.

This will enhance the power and efficiency and manipulating unstructured data.

However why we want to start with frequent pattern mining?

The general principle is we want to explore information redundancy and

use data-driven criteria to determine phrase boundaries and

the salience instead of using laws of curation and training data set.

The general methodology is to explore three ideas.

One is frequent pattern mining and a colocation analysis.

The second one is explore phrasal segmentation in documents.

The third one is we will have some quality phrase assessment criteria.

Using this, we will be able to work out efficient and

effective phrase mining methods, okay.

We are going to introduce two interesting methods developed just recently.

One is ToPMine, is mining quality phrases without training date.

This one was developed by Ahmed El-Kishky and his collaborators.

The second one was SegPhrase, is mining quality phrases with tiny training sets

has been developed by Jialu Liu and his collaborators.

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